Sexual and gender minority health: a roadmap for developing evidence-based medical school curricula
Bibliographic record
Abstract
Background: Educating future physicians about sexual and gender minority (SGM) patients and their health care needs is an important way to mitigate discrimination and health disparities faced by this community. Canada, across its 17 medical schools, lacks a national standard for teaching this essential topic. This paper aims to review the best practices for teaching an SGM curriculum in undergraduate medical education and synthesize this information into actionable propositions for curriculum development. Methods: A scoping literature review was conducted to identify best practices for SGM teaching. The review elicited peer-reviewed and grey literature on best practices for SGM teaching, policy documents, and opinion pieces from medical education authorities and SGM advocacy groups. Through an iterative process with all authors, the Canadian Queer Medical Students Association (CQMSA), and the Association of Faculties of Medicine of Canada (AFMC), a set of propositions was developed. Results: The search yielded 1347 papers, of which 89 were kept for data extraction. The main outcomes of these papers were sorted along five repeating themes, which formed the basis for six propositions; two more propositions were then added after discussion with all authors. Conclusion: We present eight propositions for the development of a national standard for SGM education at the undergraduate medical level. These include standardizing learning objectives across all schools, using established curricular models to guide curriculum development, interweaving concepts across all levels of training, diversifying teaching modalities, providing faculty training, ensuring a safe space for SGM students and faculty, using OSCEs as a teaching tool, and involving the local SGM community in curriculum development and delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.235 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".